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Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

147
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
147

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Related Experiment Video

Updated: Sep 16, 2025

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Intelligent Optimization Control of Plate Plan-View Pattern Based on Intermediate Slab Pattern Vision Inspection and

Zhong Zhao1, Chujie Liu1, Jiawei Wang1

  • 1State Key Laboratory of Digital Steel, Northeastern University, Shenyang 110819, China.

Materials (Basel, Switzerland)
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel BWO-DNN model for predicting plate crop patterns, significantly reducing irregular areas in plate heads and tails. This intelligent approach enhances plate yield by optimizing plan-view pattern control.

Keywords:
BWO-DNNPVPCintermediate slabmachine learningplate

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Area of Science:

  • Materials Science and Engineering
  • Manufacturing Processes
  • Artificial Intelligence in Industry

Background:

  • Plate production yield is significantly impacted by crop-cutting and edge losses.
  • Accurate prediction and control of plate plan-view patterns are crucial for minimizing waste.

Purpose of the Study:

  • To develop an intelligent model for predicting plate plan-view patterns.
  • To optimize control parameters for plate plan-view patterns.
  • To reduce material loss in plate manufacturing.

Main Methods:

  • A detection scheme using image processing to capture plan-view patterns of intermediate slabs and finished plates.
  • Development of a prediction and control model using the Beluga Whale Optimization-Deep Neural Network (BWO-DNN) algorithm.
  • Hyperparameter optimization of the Deep Neural Network (DNN) using the BWO algorithm.

Main Results:

  • The BWO-DNN model demonstrated strong predictive and control performance, validated by goodness of fit (R²) and mean absolute error (MAE).
  • Intelligent prediction of plate crop patterns and parameter optimization for plan-view pattern control were achieved.
  • On-site verification showed a 17.2% reduction in irregular plate head areas and a 22.6% reduction in irregular plate tail areas.

Conclusions:

  • The BWO-DNN model offers an effective solution for intelligent prediction and control in plate manufacturing.
  • The proposed method significantly reduces material waste, improving overall production efficiency.
  • This intelligent system has practical applicability in industrial plate production settings.